I wonder if a video of Ben explaining success factors might be a replacement for the martial arts simile.

For now, this is a list of success factors observed in previous cohorts:

• ‘Light’ attitude
• Perseverence
• Go slow, don’t go on until you’ve understood current section
• It’s not a race or a competition with your peers
  • Hare and the tortoise
• Do all exercises
• Re-do parts of worksheet if you didn’t understand them
• Work together
• Many instances of people who overcame initial lack of confidence to enjoy R
• Practise - learn by doing

RStudio is a user interface to R, which is a computer language that is primarily designed for data analysis and visualisation. R is a text-based language, so you interact with it by typing and then running commands.

R Studio makes it easy to run R commands and organise your work. For example, you can do simple arithmetic [2 * 221], generate some random numbers [(rnorm(10, 0,1))], and plot some random numbers [hist(rnorm(100, 0,1))].

You should think of R as a robot. The robot is extremely fast, powerful and tireless, but it’s also literal-minded, and won’t think for itself or take the initiative. You need to tell it exactly what to do, by providing very precise instructions.

R Markdown is a way of combining R with narrative text. It allows you to integrate the results of your data analysis into high quality reports, research papers, dissertations or books. Because it’s such a powerful tool, this module provides an early, gentle introduction to R Markdown.

R Markdown documents can be used interactively to run R code. If you [click on the lifesavr folder in the Files pane], you’ll notice than some files have the extension .rmd. These are R Markdown files. The file extension .rmd (or .Rmd) is important, because this is how R Studio knows that the files contain a mixture of R code and narrative text.

RStudio needs to distinguish R code from narrative text. This is done by putting the code inside some special characters, creating what’s referred to as a chunk. A chunk is opened using the symbols ```{r}, and closed using the symbols ```. This is what a chunk looks like in RStudio:

A code chunk in the RMarkdown editor

NOTE: The symbols which start and end a chunk are backticks, not single quotes.

Can’t find the backticks on your keyboard?

On windows

On a Mac

By loading ‘packages,’ you can add commands and datasets to R. Packages are a powerful feature which all R to be extended to analyse or plot data in any way imaginable. A package (sometimes called a library) is an extension to R that adds new commands and/or datasets. Packages are loaded using the library() command.

The first command you ran above was library(tidyverse). This loaded the commands needed to create the scatter plot, and also the diamonds data. The tidyverse package is so fundamental to this course that library(tidyverse) is likely to be the first line of R in the first chunk of each of your R Markdown files.

Error in diamonds %>% ggplot(aes(carat, price, colour = clarity)) : 
  could not find function "%>%"

A dataset is a set of data relating to a particular topic. Most datasets we will be working with consist of rows and columns, just like a spreadsheet. In R this type of data is stored in a special type of variable called a data.frame. You will also see references to datasets as tibbles. A tibble is just a special type of data.frame, so you can treat the two types of variable as being equivalent.

One data.frame that is built-in to R is called mtcars. This is a dataset about cars that was published in a US magazine called Motor Trend. Let’s display this data in using a new chunk. As we did with the diamonds tibble, if we type mtcars, select the variable name and execute it, we can see the data it contains.

By default this displays only the first ten rows and columns of the data. You can see other rows using the Next, Previous and number buttons below the data. You can see additional columns using the arrow next to the final, right-hand column.

You already know that the diamonds dataset was loaded using library(tidyverse). The gapminder package includes a tibble that contains data about life expectancy, GDP per capita, and population by country. We can load and explore this dataset in a the same way we loaded diamonds dataset. We load the gapminder package, type the name of the tibble (also gapminder) and run it. Again, we can use the navigation buttons to explore the data.

A scatterplot shows the relationship between two variables by plotting their values as points.

This chunk creates a scatter plot by piping the mpg data into a ggplot command. The plot is built in two steps. The first step, ggplot(aes(cty, hwy)) selects variables for the x and y axes. In this case, miles per gallon (mpg) when cars are driven in the city (cty) will be the x-axis, and mpg when cars are driven on the highway (hwy) will be the y-axis. We can [see the axes by running just this part of the command].

In ggplot, each step is separated by + and goes on a new line. Because R Studio knows this is all part of the same ‘pipeline,’ it automatically indents the code.

The second step geom_point() is a command which plots the data as points. If we [run the chunk], we see a scatter plot. A point is plotted for each row, using the values for the cty and hwy variables.

Boxplots are useful for visualising categorical data. In a boxplot, the thick line is the median. That thick line is enclosed inside a rectangle (the ‘box’), and the size of the box indicates the inter-quartile range (IQR). The IQR contains the middle 50% of the ordered data. A wider IQR indicates greater variation in a dataset.

The top and bottom of the box are called ‘hinges.’ The vertical lines connected to each hinge are called ‘whiskers,’ and give some indication of the broader range of the data. Exactly what the whiskers show differs depending on the particular command you use to draw a boxplot. In this case, the upper whisker shows the largest data point that is no more than 1.5 times the IQR above the upper hinge. The lower whisker is the lowest point no more than 1.5x the IQR below the lower hinge. In this version of a boxplot, any data point outside the range of the whiskers is described as an ‘outlying point’ and is shown individually as a dot.

Colour can be added to a scatterplot to categorise the points. So far, you’ve used the aes command to define which variable is plotted on the x and y axes. The aes command is short for ‘aesthetics,’ and it is a way to map variables to visual aspects of a plot. In addition to mapping variables to the x and y axes, the colour option maps colours to a categorical variable.

We could enhance the scatterplot showing mpg for city and highway driving, by adding a colour for each drivetrain.

mpg %>%
  ggplot(aes(cty, hwy, color = drv)) +
  geom_point()

Markdown uses special characters to style text in the same way you might use menus in a word processor to define headings, font styles, lists etc. Your workbook contains examples of the main markdown commands you will need:

• The # at the start of # lifesaveR: Workbook 1 assigns the text lifesaveR: Workbook 1 as a first level heading (the largest size)
• The sentence below that heading is ordinary text
• ## An example plot is a second level heading (next largest). Using ### and #### makes even smaller headings
• **bold text** makes bold text
• *italic text* makes italic text
• The lines beginning 1. under # Exercise 1 create a numbered list, starting at 1

The Knit button combines the markdown and R chunks, ‘knitting’ them together into an output document. It works through your document converting markdown to formatted text, and running each of your R chunks, in the order you have written them. You can see that this is the case by ‘knitting’ the R Markdown workbook for this session.

The following simple approach will give you regular practice writing markdown:

1. Each time you reach a new section in a worksheet, copy the section name (e.g. ‘Exploring and checking data’) into a level 2 heading in your workbook
2. Before starting each exercise, create a level 3 heading for the exercise number [e.g. ### Exercise 5]
3. Copy the exercise instructions below this as a bullet list
4. Add any additional notes that remind you what you have learnt in this section [e.g. Some data is *built-in* to R.]
5. Complete the exercise.

This methodical approach will allow you to complete each workbook step by step. At the end of each session, you will have written a neat, ‘summary’ document which will be a useful revision aid. The same approach works for all R worksheets at the University of Plymouth. If you get into this habit, you will be adding to your ‘reference library’ whenever you complete a worksheet. These summary documents are invaluable when you are more familiar with R but need to quickly remind yourself how to use a particular feature.

The [outline button] in R Studio shows you an outline of your document using the headings you have defined. This makes it easy to find a particular topic and the exercises you have completed to learn about each topic.

The R Markdown Cheat Sheet is a useful quick reference for markdown syntax.